portfolio-02
Ben Hardin
2023-02-07
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
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## ✔ readr 2.1.3 ✔ forcats 0.5.2
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## Attaching package: 'plotly'
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## last_plot
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## filter
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## layoutgoals <- read.csv("data/TransposedGoals.csv")blah blah
ggplot(data = goals, aes(x = Avg_DisLikelihood, y = Avg_HonLikelihood))+
geom_point()+
theme_bw()+
labs(title = "Scatterplot of Goal Expectancies",
x = "Likelihood that dishonesty would achieve goal",
y = "Likelihood that honesty would achieve goal")
ggsave("goals_scatter.png", plot = last_plot(), device = "png")## Saving 4 x 4 in imagegoals %>%
summarize(r = cor(Avg_DisLikelihood, Avg_HonLikelihood))## r
## 1 -0.8360762blah blah the 2nd
ggplot(goals, aes(x = Diff_Likelihood))+
geom_histogram(fill = "grey", color = "black")+
theme_bw()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
goals %>%
summary## CASE_LBL R1_Hon R2_Hon R3_Hon
## Length:155 Min. :1.000 Min. :1.000 Min. :1.000
## Class :character 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.000
## Mode :character Median :3.000 Median :3.000 Median :4.000
## Mean :2.948 Mean :3.065 Mean :3.523
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000
##
## R4_Hon R5_Hon R6_Hon R7_Hon Goal
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00 Min. : 1.0
## 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:2.00 1st Qu.: 39.5
## Median :2.000 Median :4.000 Median :3.000 Median :3.00 Median : 78.0
## Mean :2.755 Mean :3.574 Mean :3.271 Mean :3.09 Mean : 78.0
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:4.00 3rd Qu.:116.5
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.00 Max. :155.0
##
## R1_Dis R2_Dis R3_Dis R4_Dis
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :3.000 Median :4.000
## Mean :3.084 Mean :2.619 Mean :3.135 Mean :3.226
## 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
##
## R5_Dis R6_Dis R7_Dis Avg_HonLikelihood
## Min. :1.000 Min. :1.000 Min. :1 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2 1st Qu.:2.429
## Median :4.000 Median :3.000 Median :3 Median :3.000
## Mean :3.497 Mean :2.604 Mean :3 Mean :3.175
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:4 3rd Qu.:4.143
## Max. :5.000 Max. :5.000 Max. :5 Max. :5.000
## NA's :1
## Avg_DisLikelihood Diff_Likelihood
## Min. :1.000 Min. :-4.0000
## 1st Qu.:2.000 1st Qu.:-1.4286
## Median :3.286 Median :-0.2857
## Mean :3.023 Mean : 0.1524
## 3rd Qu.:3.857 3rd Qu.: 2.1429
## Max. :5.000 Max. : 4.0000
## blah blah the 3rd
polar_goals <- goals %>%
filter(Diff_Likelihood >= 2.1 | Diff_Likelihood <= -1.4,
Avg_HonLikelihood >= 3.0 | Avg_HonLikelihood <= 2.4,
Avg_DisLikelihood >= 3.2 | Avg_DisLikelihood <= 2.00)
ggplot(data = polar_goals, aes(x = Avg_DisLikelihood, y = Avg_HonLikelihood))+
geom_point(position = "jitter")+
theme_bw()
polar_goals <- polar_goals %>%
mutate(likelihood = if_else(Avg_HonLikelihood > 2.99, "Honesty", "Dishonesty"))polar_goals %>%
group_by(likelihood) %>%
count()## # A tibble: 2 × 2
## # Groups: likelihood [2]
## likelihood n
## <chr> <int>
## 1 Dishonesty 28
## 2 Honesty 36polar_goals %>%
filter(likelihood == "Honesty") %>%
arrange(desc(Diff_Likelihood)) %>%
arrange(desc(Avg_HonLikelihood))## CASE_LBL R1_Hon R2_Hon R3_Hon R4_Hon R5_Hon R6_Hon R7_Hon Goal
## 1 Hon_CorrectInaccurate 5 5 5 5 5 5 5 57
## 2 Hon_AvoidLiar 5 5 5 5 5 5 5 24
## 3 Hon_NeedToKnow 4 5 5 5 5 5 5 12
## 4 Hon_ExpFeelings 4 5 5 5 5 5 5 8
## 5 Hon_ImpInfo 4 5 5 5 5 5 5 27
## 6 Hon_BeYourself 5 5 5 5 5 5 4 70
## 7 Hon_ExpressHowFeel 4 5 5 5 5 5 5 74
## 8 Hon_Self_Image 4 4 5 5 5 5 5 48
## 9 Hon_AvoidViolat 4 4 5 5 5 5 5 37
## 10 Hon_ShareMyself 5 4 5 5 5 5 4 49
## 11 Hon_BeAuth 4 4 5 5 5 5 4 73
## 12 Hon_ShowHonesty 4 4 5 4 5 5 5 9
## 13 Hon_Chest 3 5 5 5 5 4 5 28
## 14 Hon_AvoidFake 4 4 5 4 5 5 5 61
## 15 Hon_RightThing 4 4 5 4 5 5 5 2
## 16 Hon_AvoidConf 4 4 5 4 5 5 5 43
## 17 Hon_FosterUnd 4 4 4 5 5 5 5 104
## 18 Hon_CommClear 4 5 4 4 5 5 5 66
## 19 Hon_AvoidGross 5 1 5 5 5 5 5 33
## 20 Hon_AcknFault 4 4 5 4 4 5 5 1
## 21 Hon_BeBestSelf 4 3 5 4 5 5 5 23
## 22 Hon_GainTrust 3 4 4 5 5 5 5 22
## 23 Hon_AvoidPollut 5 3 5 4 5 5 4 34
## 24 Hon_TreatResp 5 3 4 5 5 5 4 69
## 25 Hon_AvoidMis 4 4 4 5 5 4 4 17
## 26 Hon_ReduUncertainty 4 4 4 3 5 5 5 41
## 27 Hon_ConveyInfo 3 3 5 4 5 5 5 46
## 28 Hon_GoodEx 4 3 4 4 5 5 5 16
## 29 Hon_BeUnd 3 3 4 5 5 5 5 65
## 30 Hon_EffecCoord 2 4 4 4 5 5 5 45
## 31 Hon_HelpDoBett 4 4 4 4 3 5 5 51
## 32 Hon_HelpProduc 3 4 5 3 5 5 4 71
## 33 Hon_VoidCov 4 1 4 5 5 5 5 6
## 34 Hon_FacilWork 3 4 4 4 5 5 4 39
## 35 Hon_HelpImprov 4 3 4 4 5 5 4 40
## 36 Hon_FacilPlan 3 3 4 4 5 5 4 21
## R1_Dis R2_Dis R3_Dis R4_Dis R5_Dis R6_Dis R7_Dis Avg_HonLikelihood
## 1 1 1 1 1 1 1 1 5.000000
## 2 1 1 1 2 1 1 1 5.000000
## 3 2 1 1 1 1 1 1 4.857143
## 4 3 1 1 1 1 1 1 4.857143
## 5 2 1 2 1 1 1 1 4.857143
## 6 2 1 1 1 1 1 2 4.857143
## 7 2 1 2 1 1 1 1 4.857143
## 8 2 2 1 2 1 1 1 4.714286
## 9 2 2 2 1 1 2 3 4.714286
## 10 3 2 1 5 1 1 1 4.714286
## 11 1 1 1 1 1 1 1 4.571429
## 12 3 2 1 1 1 1 1 4.571429
## 13 4 1 2 1 1 1 1 4.571429
## 14 2 2 2 1 2 1 1 4.571429
## 15 2 2 2 1 2 2 1 4.571429
## 16 2 2 1 2 2 2 1 4.571429
## 17 4 2 2 1 1 1 1 4.571429
## 18 3 2 2 2 2 1 1 4.571429
## 19 1 1 1 1 1 1 1 4.428571
## 20 4 2 1 1 1 1 1 4.428571
## 21 3 2 1 1 1 2 1 4.428571
## 22 3 2 2 2 2 2 1 4.428571
## 23 2 3 2 2 1 2 2 4.428571
## 24 2 3 2 1 2 2 2 4.428571
## 25 2 2 2 2 1 1 1 4.285714
## 26 2 2 2 2 1 1 1 4.285714
## 27 3 3 1 1 1 1 1 4.285714
## 28 2 2 2 2 2 1 1 4.285714
## 29 3 3 2 1 2 1 1 4.285714
## 30 3 2 2 1 1 1 1 4.142857
## 31 2 2 1 2 1 2 2 4.142857
## 32 3 2 2 1 1 2 2 4.142857
## 33 3 1 2 2 2 3 1 4.142857
## 34 3 2 2 1 1 2 3 4.142857
## 35 2 2 2 2 2 2 2 4.142857
## 36 3 2 2 1 1 1 2 4.000000
## Avg_DisLikelihood Diff_Likelihood likelihood
## 1 1.000000 4.000000 Honesty
## 2 1.142857 3.857143 Honesty
## 3 1.142857 3.714286 Honesty
## 4 1.285714 3.571429 Honesty
## 5 1.285714 3.571429 Honesty
## 6 1.285714 3.571429 Honesty
## 7 1.285714 3.571429 Honesty
## 8 1.428571 3.285714 Honesty
## 9 1.857143 2.857143 Honesty
## 10 2.000000 2.714286 Honesty
## 11 1.000000 3.571429 Honesty
## 12 1.428571 3.142857 Honesty
## 13 1.571429 3.000000 Honesty
## 14 1.571429 3.000000 Honesty
## 15 1.714286 2.857143 Honesty
## 16 1.714286 2.857143 Honesty
## 17 1.714286 2.857143 Honesty
## 18 1.857143 2.714286 Honesty
## 19 1.000000 3.428571 Honesty
## 20 1.571429 2.857143 Honesty
## 21 1.571429 2.857143 Honesty
## 22 2.000000 2.428571 Honesty
## 23 2.000000 2.428571 Honesty
## 24 2.000000 2.428571 Honesty
## 25 1.571429 2.714286 Honesty
## 26 1.571429 2.714286 Honesty
## 27 1.571429 2.714286 Honesty
## 28 1.714286 2.571429 Honesty
## 29 1.857143 2.428571 Honesty
## 30 1.571429 2.571429 Honesty
## 31 1.714286 2.428571 Honesty
## 32 1.857143 2.285714 Honesty
## 33 2.000000 2.142857 Honesty
## 34 2.000000 2.142857 Honesty
## 35 2.000000 2.142857 Honesty
## 36 1.714286 2.285714 Honestypolar_goals %>%
filter(likelihood == "Dishonesty") %>%
arrange(Diff_Likelihood) %>%
arrange(desc(Avg_DisLikelihood))## CASE_LBL R1_Hon R2_Hon R3_Hon R4_Hon R5_Hon R6_Hon R7_Hon Goal
## 1 Hon_GetSometh 1 1 1 1 1 1 1 98
## 2 Hon_BeSeeBett 2 1 1 1 1 1 1 142
## 3 Hon_KeepLieGo 1 1 1 1 1 1 1 130
## 4 Hon_HidePers 1 2 1 1 1 1 1 75
## 5 Hon_CoverUp 3 1 2 1 1 1 1 117
## 6 Hon_AvoidArgu 2 2 2 1 2 2 2 108
## 7 Hon_AvoidVuln 2 2 1 1 1 3 2 121
## 8 Hon_AvoidBad 2 2 2 2 1 2 3 136
## 9 Hon_ProtFeelings 2 3 2 1 3 2 2 13
## 10 Hon_TrickSomeone 2 1 1 1 1 1 1 122
## 11 Hon_AvoidOverre 2 3 2 1 2 1 2 93
## 12 Hon_Secret 5 1 2 1 2 2 1 76
## 13 Hon_AvoidUncomf 2 2 3 1 3 2 1 91
## 14 Hon_AvoidAwk 2 3 2 1 2 2 3 83
## 15 Hon_AvoidReje 2 2 2 1 3 2 2 140
## 16 Hon_LookBetter 2 3 3 2 2 1 2 31
## 17 Hon_DecMyself 1 1 1 1 1 1 1 105
## 18 Hon_ProtPriv 1 2 3 1 3 2 2 101
## 19 Hon_AvoidCorrec 2 1 2 1 3 4 2 103
## 20 Hon_AvoidWaves 2 2 4 3 2 2 1 55
## 21 Hon_AvoidOther 2 3 3 2 1 1 1 147
## 22 Hon_StopConv 2 2 3 2 2 2 1 109
## 23 Hon_PresrvEng 2 3 2 1 2 1 3 115
## 24 Hon_KeepBrief 2 3 3 1 2 2 1 151
## 25 Hon_ConfToRole 2 2 2 1 3 3 2 155
## 26 Hon_Surprise 3 3 3 1 1 1 1 77
## 27 Hon_ConfuseOthers 2 1 2 1 2 1 1 99
## 28 Hon_ConvMyse 2 2 1 1 3 1 2 106
## R1_Dis R2_Dis R3_Dis R4_Dis R5_Dis R6_Dis R7_Dis Avg_HonLikelihood
## 1 5 5 5 5 5 5 5 1.000000
## 2 5 5 5 4 5 5 4 1.142857
## 3 5 4 4 4 5 5 5 1.000000
## 4 4 3 5 5 5 5 5 1.142857
## 5 4 4 5 4 5 5 5 1.428571
## 6 4 4 5 5 5 5 4 1.857143
## 7 4 4 5 5 5 3 5 1.714286
## 8 4 4 5 4 5 5 4 2.000000
## 9 4 4 4 5 5 5 4 2.142857
## 10 4 4 5 3 5 5 4 1.142857
## 11 4 4 5 4 5 4 3 1.857143
## 12 2 5 5 5 5 2 5 2.000000
## 13 3 3 5 5 5 4 4 2.000000
## 14 4 3 5 5 5 3 4 2.142857
## 15 4 2 5 5 4 4 4 2.000000
## 16 3 3 5 5 5 3 4 2.142857
## 17 5 5 3 4 3 3 4 1.000000
## 18 3 4 4 5 5 2 4 2.000000
## 19 4 2 4 5 5 3 4 2.142857
## 20 4 3 4 4 5 3 4 2.285714
## 21 4 2 4 4 5 3 4 1.857143
## 22 3 3 4 4 5 3 4 2.000000
## 23 3 3 4 4 5 3 4 2.000000
## 24 3 2 4 4 5 4 4 2.000000
## 25 4 3 4 5 4 2 4 2.142857
## 26 3 3 4 4 5 2 4 1.857143
## 27 3 3 4 1 4 4 5 1.428571
## 28 3 3 5 4 3 1 4 1.714286
## Avg_DisLikelihood Diff_Likelihood likelihood
## 1 5.000000 -4.000000 Dishonesty
## 2 4.714286 -3.571429 Dishonesty
## 3 4.571429 -3.571429 Dishonesty
## 4 4.571429 -3.428571 Dishonesty
## 5 4.571429 -3.142857 Dishonesty
## 6 4.571429 -2.714286 Dishonesty
## 7 4.428571 -2.714286 Dishonesty
## 8 4.428571 -2.428571 Dishonesty
## 9 4.428571 -2.285714 Dishonesty
## 10 4.285714 -3.142857 Dishonesty
## 11 4.142857 -2.285714 Dishonesty
## 12 4.142857 -2.142857 Dishonesty
## 13 4.142857 -2.142857 Dishonesty
## 14 4.142857 -2.000000 Dishonesty
## 15 4.000000 -2.000000 Dishonesty
## 16 4.000000 -1.857143 Dishonesty
## 17 3.857143 -2.857143 Dishonesty
## 18 3.857143 -1.857143 Dishonesty
## 19 3.857143 -1.714286 Dishonesty
## 20 3.857143 -1.571429 Dishonesty
## 21 3.714286 -1.857143 Dishonesty
## 22 3.714286 -1.714286 Dishonesty
## 23 3.714286 -1.714286 Dishonesty
## 24 3.714286 -1.714286 Dishonesty
## 25 3.714286 -1.571429 Dishonesty
## 26 3.571429 -1.714286 Dishonesty
## 27 3.428571 -2.000000 Dishonesty
## 28 3.285714 -1.571429 DishonestyTrying to make a fancy plot
So, first my goal is the make an interactive version of our scatterplot from earlier, that tells you the name of each goal when you hover over it. I also want to use color to more clearly emphasize the size of the difference between honesty and dishonesty scores for each goal.
Using the plotly package, I was able to make an interactive plot. To make it show the actual name of each goal, I had to specify a new aesthetic “label” for the tooltip to use. To get the points to have the gradient effect I wanted, I also had to create an absolute value version of the difference scores, so that goals that are more “polar” will be lighter than more ambivalent goals, regardless of whether they are pulling for the honest or the dishonest pole.
#absolute value differences
goals <- goals %>%
mutate(abs_diff = abs(Diff_Likelihood))
goals_interactive <- goals %>%
ggplot(aes(x = Avg_DisLikelihood, y = Avg_HonLikelihood, label = CASE_LBL))+
geom_point(aes(color = abs_diff), position = "jitter")+
theme_bw()
ggplotly(tooltip = c("label"))I’m so excited about this plot! The only problem with it is that it is using names from the csv file, which are not consistently helpful for telling us what each goal actually represents.
Trying another one
What I would like to do now is select a few exemplar goals, which seem to (a) tap into most of the important themes/contents of goals and (b) represent a goals from the range of honesty vs. dishonesty
goals %>%
arrange(Diff_Likelihood)## CASE_LBL R1_Hon R2_Hon R3_Hon R4_Hon R5_Hon R6_Hon R7_Hon Goal
## 1 Hon_GetSometh 1 1 1 1 1 1 1 98
## 2 Hon_KeepLieGo 1 1 1 1 1 1 1 130
## 3 Hon_BeSeeBett 2 1 1 1 1 1 1 142
## 4 Hon_HidePers 1 2 1 1 1 1 1 75
## 5 Hon_CoverUp 3 1 2 1 1 1 1 117
## 6 Hon_TrickSomeone 2 1 1 1 1 1 1 122
## 7 Hon_DecMyself 1 1 1 1 1 1 1 105
## 8 Hon_AvoidArgu 2 2 2 1 2 2 2 108
## 9 Hon_AvoidVuln 2 2 1 1 1 3 2 121
## 10 Hon_AvoidBad 2 2 2 2 1 2 3 136
## 11 Hon_ProtFeelings 2 3 2 1 3 2 2 13
## 12 Hon_AvoidOverre 2 3 2 1 2 1 2 93
## 13 Hon_Secret 5 1 2 1 2 2 1 76
## 14 Hon_AvoidUncomf 2 2 3 1 3 2 1 91
## 15 Hon_AvoidJudg 2 2 2 2 3 3 3 80
## 16 Hon_AvoidAwk 2 3 2 1 2 2 3 83
## 17 Hon_AvoidDoing 3 4 3 1 2 3 2 95
## 18 Hon_ConfuseOthers 2 1 2 1 2 1 1 99
## 19 Hon_AvoidPunish 2 4 2 2 3 2 3 118
## 20 Hon_AvoidReje 2 2 2 1 3 2 2 140
## 21 Hon_LookBetter 2 3 3 2 2 1 2 31
## 22 Hon_AvoidAng 2 3 3 4 2 3 2 84
## 23 Hon_AvoidAwkwardness 2 3 3 2 3 2 2 96
## 24 Hon_ProtPriv 1 2 3 1 3 2 2 101
## 25 Hon_AvoidOther 2 3 3 2 1 1 1 147
## 26 Hon_MakeFeelBet 2 3 3 2 3 2 2 148
## 27 Hon_Surprise 3 3 3 1 1 1 1 77
## 28 Hon_AvoidCorrec 2 1 2 1 3 4 2 103
## 29 Hon_StopConv 2 2 3 2 2 2 1 109
## 30 Hon_PresrvEng 2 3 2 1 2 1 3 115
## 31 Hon_AvoidOtherDisap 2 3 3 2 3 4 2 132
## 32 Hon_KeepBrief 2 3 3 1 2 2 1 151
## 33 Hon_AvoidWaves 2 2 4 3 2 2 1 55
## 34 Hon_ConvMyse 2 2 1 1 3 1 2 106
## 35 Hon_AvoidEmb 2 3 3 3 2 2 3 112
## 36 Hon_ConfToRole 2 2 2 1 3 3 2 155
## 37 Hon_EnsurSafe 2 3 3 1 2 3 3 78
## 38 Hon_KeepPeace 2 3 3 3 2 3 2 82
## 39 Hon_BePolite 4 2 3 2 2 2 2 124
## 40 Hon_GentleOther 2 3 3 2 3 2 2 126
## 41 Hon_AvoidStandOut 2 2 4 1 3 1 3 54
## 42 Hon_Story 3 3 3 4 2 2 2 90
## 43 Hon_AvoidSuff 3 2 3 1 4 2 3 102
## 44 Hon_PleaseOther 2 3 3 2 5 3 2 125
## 45 Hon_GetAhead 3 2 3 2 3 2 3 145
## 46 Hon_ProtectWorr 2 3 4 1 3 2 3 149
## 47 Hon_GetWhatIWant 3 4 3 2 3 3 3 81
## 48 Hon_AvoidHassle 2 3 4 1 4 2 3 94
## 49 Hon_ControlOther 3 3 3 2 2 3 2 111
## 50 Hon_Strategy 2 1 2 2 2 1 2 116
## 51 Hon_AvoidLose 3 3 3 2 3 3 3 134
## 52 Hon_AvoidDisap 2 3 4 2 3 5 2 29
## 53 Hon_AvoidDisSomeone 2 3 3 2 2 4 3 89
## 54 Hon_GetAtten 3 4 4 2 3 2 3 97
## 55 Hon_ExplMyself 2 2 2 1 5 1 4 114
## 56 Hon_EntertSelf 3 2 3 1 1 2 2 123
## 57 Hon_HurtReput 3 3 3 3 3 3 3 129
## 58 Hon_SeemSmart 3 3 3 2 4 2 3 131
## 59 Hon_GetAct 2 4 3 2 3 3 2 133
## 60 Hon_HurtOther 2 3 4 3 3 3 3 26
## 61 Hon_FollowScript 4 2 4 1 2 3 2 53
## 62 Hon_ToLookGood 3 3 3 3 4 3 3 110
## 63 Hon_KeepSmooth 3 2 3 2 3 3 2 138
## 64 Hon_MakeLike 3 2 3 3 3 2 2 139
## 65 Hon_SeemFriendly 2 4 3 2 3 2 2 143
## 66 Hon_GetEven 3 3 4 4 2 2 2 153
## 67 Hon_GetWant 2 4 3 2 4 5 3 4
## 68 Hon_ProtEmb 3 2 4 4 3 4 2 11
## 69 Hon_SeemGen 3 3 3 3 3 4 2 144
## 70 Hon_AvoidAccu 1 3 4 5 5 3 2 146
## 71 Hon_SayExpec 3 2 4 2 4 3 2 154
## 72 Hon_DisapParents 2 3 4 2 3 4 4 25
## 73 Hon_BeAttrac 3 3 2 2 4 2 2 32
## 74 Hon_FeelTrue 1 1 3 1 3 3 4 107
## 75 Hon_NotFeelBad 2 3 4 1 5 4 2 79
## 76 Hon_FollowNorms.0 4 4 4 2 4 3 2 86
## 77 Hon_PositvDir 3 3 3 2 3 3 2 120
## 78 Hon_HowToAct 3 3 4 1 4 1 1 135
## 79 Hon_Consequences 2 1 4 2 3 2 3 3
## 80 Hon_BeKind 2 3 3 3 5 4 3 14
## 81 Hon_FollowNorms 3 3 4 2 4 4 2 18
## 82 Hon_HelpGetSom 3 3 5 1 3 3 3 50
## 83 Hon_ProtectRel 3 4 4 3 5 5 1 119
## 84 Hon_GetOtherDo 2 5 4 2 4 5 2 141
## 85 Hon_ChangeBeh 3 4 4 3 3 4 3 85
## 86 Hon_GainConfi 2 4 4 3 4 3 3 100
## 87 Hon_ContinConvo 4 3 3 4 3 2 2 150
## 88 Hon_AvProb 4 4 4 3 4 5 3 5
## 89 Hon_KeepFromEmb 3 2 4 4 3 5 3 15
## 90 Hon_BeHumb 3 3 4 2 3 1 3 59
## 91 Hon_FacilConvo.0 3 3 3 3 4 3 2 87
## 92 Hon_WasteTime 3 3 4 3 5 4 4 92
## 93 Hon_ImprEffic 3 2 3 2 5 4 5 42
## 94 Hon_AvoidSusp 3 3 4 3 5 1 4 44
## 95 Hon_FacilConvo 4 3 3 2 5 3 3 20
## 96 Hon_GetDeserve 4 4 4 2 5 4 3 47
## 97 Honesty_137 2 3 5 3 5 5 3 137
## 98 Hon_ActNat 4 3 4 2 5 4 3 62
## 99 Hon_BeBest 3 2 3 2 5 5 5 88
## 100 Hon_DrawAtten 3 4 4 2 5 2 2 113
## 101 Hon_Autonomy 4 3 4 3 4 2 5 128
## 102 Hon_PutInPlace 4 4 4 4 3 2 4 56
## 103 Hon_BeResp 4 4 4 4 5 4 4 60
## 104 Hon_MakeFair 4 3 4 2 5 4 2 127
## 105 Hon_ShowFairness 3 3 4 4 4 4 3 10
## 106 Hon_AvoidGuilt 2 4 4 4 5 5 4 19
## 107 Hon_GetHonesty 4 4 4 4 4 4 4 38
## 108 Hon_MakeWorth 3 3 4 4 3 4 4 63
## 109 Hon_RecogProb 4 3 5 2 2 1 4 7
## 110 Hon_KeepFirm 3 3 4 3 3 3 5 35
## 111 Hon_SeekHelp 3 4 5 5 4 3 4 58
## 112 Hon_FeelClose 3 3 4 4 4 5 5 152
## 113 Hon_BeAware 4 4 4 1 5 1 5 36
## 114 Hon_MakeMean 3 4 4 4 4 4 4 64
## 115 Hon_VoidCov 4 1 4 5 5 5 5 6
## 116 Hon_AvBreakTrust 4 4 5 5 5 5 2 30
## 117 Hon_FacilWork 3 4 4 4 5 5 4 39
## 118 Hon_HelpImprov 4 3 4 4 5 5 4 40
## 119 Hon_HelpSolveProb 4 4 4 4 5 5 4 52
## 120 Hon_Deepen 3 4 4 5 4 5 5 67
## 121 Hon_LongRun 4 5 4 4 5 5 4 72
## 122 Hon_FacilPlan 3 3 4 4 5 5 4 21
## 123 Hon_AvoidCaught 5 4 4 4 5 5 5 68
## 124 Hon_HelpProduc 3 4 5 3 5 5 4 71
## 125 Hon_GainTrust 3 4 4 5 5 5 5 22
## 126 Hon_AvoidPollut 5 3 5 4 5 5 4 34
## 127 Hon_HelpDoBett 4 4 4 4 3 5 5 51
## 128 Hon_BeUnd 3 3 4 5 5 5 5 65
## 129 Hon_TreatResp 5 3 4 5 5 5 4 69
## 130 Hon_GoodEx 4 3 4 4 5 5 5 16
## 131 Hon_EffecCoord 2 4 4 4 5 5 5 45
## 132 Hon_AvoidMis 4 4 4 5 5 4 4 17
## 133 Hon_ReduUncertainty 4 4 4 3 5 5 5 41
## 134 Hon_ConveyInfo 3 3 5 4 5 5 5 46
## 135 Hon_ShareMyself 5 4 5 5 5 5 4 49
## 136 Hon_CommClear 4 5 4 4 5 5 5 66
## 137 Hon_AcknFault 4 4 5 4 4 5 5 1
## 138 Hon_RightThing 4 4 5 4 5 5 5 2
## 139 Hon_BeBestSelf 4 3 5 4 5 5 5 23
## 140 Hon_AvoidViolat 4 4 5 5 5 5 5 37
## 141 Hon_AvoidConf 4 4 5 4 5 5 5 43
## 142 Hon_FosterUnd 4 4 4 5 5 5 5 104
## 143 Hon_Chest 3 5 5 5 5 4 5 28
## 144 Hon_AvoidFake 4 4 5 4 5 5 5 61
## 145 Hon_ShowHonesty 4 4 5 4 5 5 5 9
## 146 Hon_Self_Image 4 4 5 5 5 5 5 48
## 147 Hon_AvoidGross 5 1 5 5 5 5 5 33
## 148 Hon_ExpFeelings 4 5 5 5 5 5 5 8
## 149 Hon_ImpInfo 4 5 5 5 5 5 5 27
## 150 Hon_BeYourself 5 5 5 5 5 5 4 70
## 151 Hon_BeAuth 4 4 5 5 5 5 4 73
## 152 Hon_ExpressHowFeel 4 5 5 5 5 5 5 74
## 153 Hon_NeedToKnow 4 5 5 5 5 5 5 12
## 154 Hon_AvoidLiar 5 5 5 5 5 5 5 24
## 155 Hon_CorrectInaccurate 5 5 5 5 5 5 5 57
## R1_Dis R2_Dis R3_Dis R4_Dis R5_Dis R6_Dis R7_Dis Avg_HonLikelihood
## 1 5 5 5 5 5 5 5 1.000000
## 2 5 4 4 4 5 5 5 1.000000
## 3 5 5 5 4 5 5 4 1.142857
## 4 4 3 5 5 5 5 5 1.142857
## 5 4 4 5 4 5 5 5 1.428571
## 6 4 4 5 3 5 5 4 1.142857
## 7 5 5 3 4 3 3 4 1.000000
## 8 4 4 5 5 5 5 4 1.857143
## 9 4 4 5 5 5 3 5 1.714286
## 10 4 4 5 4 5 5 4 2.000000
## 11 4 4 4 5 5 5 4 2.142857
## 12 4 4 5 4 5 4 3 1.857143
## 13 2 5 5 5 5 2 5 2.000000
## 14 3 3 5 5 5 4 4 2.000000
## 15 4 4 5 4 5 5 4 2.428571
## 16 4 3 5 5 5 3 4 2.142857
## 17 4 3 5 5 5 5 5 2.571429
## 18 3 3 4 1 4 4 5 1.428571
## 19 4 4 5 5 5 5 4 2.571429
## 20 4 2 5 5 4 4 4 2.000000
## 21 3 3 5 5 5 3 4 2.142857
## 22 4 4 5 5 5 5 4 2.714286
## 23 4 3 4 5 5 5 4 2.428571
## 24 3 4 4 5 5 2 4 2.000000
## 25 4 2 4 4 5 3 4 1.857143
## 26 4 4 4 5 5 4 4 2.428571
## 27 3 3 4 4 5 2 4 1.857143
## 28 4 2 4 5 5 3 4 2.142857
## 29 3 3 4 4 5 3 4 2.000000
## 30 3 3 4 4 5 3 4 2.000000
## 31 4 4 5 5 5 4 4 2.714286
## 32 3 2 4 4 5 4 4 2.000000
## 33 4 3 4 4 5 3 4 2.285714
## 34 3 3 5 4 3 1 4 1.714286
## 35 4 3 5 5 5 3 4 2.571429
## 36 4 3 4 5 4 2 4 2.142857
## 37 4 3 4 5 4 3 4 2.428571
## 38 2 4 5 4 5 4 4 2.571429
## 39 3 4 4 4 5 3 4 2.428571
## 40 3 3 5 4 5 4 3 2.428571
## 41 3 2 4 4 5 4 3 2.285714
## 42 3 3 5 5 3 4 5 2.714286
## 43 3 3 3 5 5 4 4 2.571429
## 44 4 3 4 5 4 4 5 2.857143
## 45 3 2 5 5 5 3 4 2.571429
## 46 3 4 3 4 5 4 4 2.571429
## 47 4 3 4 5 5 4 4 3.000000
## 48 3 3 4 4 5 3 5 2.714286
## 49 4 3 4 3 5 3 4 2.571429
## 50 3 1 3 2 5 2 4 1.714286
## 51 4 3 5 4 5 4 3 2.857143
## 52 4 4 3 4 5 4 4 3.000000
## 53 4 4 3 4 5 4 2 2.714286
## 54 4 3 4 5 5 3 4 3.000000
## 55 2 3 5 4 5 3 2 2.428571
## 56 3 1 4 4 3 2 4 2.000000
## 57 3 3 4 3 5 5 5 3.000000
## 58 3 3 5 5 5 2 4 2.857143
## 59 4 3 4 3 5 3 4 2.714286
## 60 3 3 4 4 5 4 4 3.000000
## 61 3 2 2 5 5 3 4 2.571429
## 62 3 3 4 5 5 3 5 3.142857
## 63 4 2 4 3 5 2 4 2.571429
## 64 3 3 3 4 5 4 2 2.571429
## 65 4 2 3 5 5 2 3 2.571429
## 66 4 3 4 3 4 3 5 2.857143
## 67 4 3 4 5 5 3 4 3.285714
## 68 4 3 3 5 5 3 4 3.142857
## 69 4 4 3 4 4 4 3 3.000000
## 70 4 3 4 4 5 4 4 3.285714
## 71 3 3 4 5 3 3 4 2.857143
## 72 4 4 2 4 5 3 4 3.142857
## 73 3 3 4 4 4 2 2 2.571429
## 74 4 3 3 3 3 1 2 2.285714
## 75 4 2 4 3 4 3 4 3.000000
## 76 3 2 3 5 5 3 4 3.285714
## 77 3 2 4 2 4 2 4 2.714286
## 78 3 2 4 2 4 2 2 2.428571
## 79 3 2 3 2 3 2 3 2.428571
## 80 2 3 4 4 4 4 3 3.285714
## 81 3 2 3 5 4 3 3 3.142857
## 82 3 2 2 4 5 2 4 3.000000
## 83 3 4 3 5 5 3 3 3.571429
## 84 3 3 4 3 5 3 4 3.428571
## 85 3 2 4 4 5 3 3 3.428571
## 86 3 3 4 4 3 3 3 3.285714
## 87 3 2 4 4 3 1 4 3.000000
## 88 3 4 2 5 5 4 3 3.857143
## 89 4 3 2 4 5 1 4 3.428571
## 90 3 3 1 4 4 1 2 2.714286
## 91 3 2 4 2 3 3 3 3.000000
## 92 2 2 4 4 5 4 3 3.714286
## 93 3 2 3 3 5 3 2 3.428571
## 94 3 3 2 3 3 2 4 3.285714
## 95 3 2 3 2 3 2 4 3.285714
## 96 3 3 3 3 5 1 3 3.714286
## 97 4 2 2 5 3 2 2 3.714286
## 98 2 2 2 2 4 2 4 3.571429
## 99 2 3 3 5 1 3 1 3.571429
## 100 2 2 3 1 2 1 4 3.142857
## 101 2 2 4 2 3 2 3 3.571429
## 102 3 2 2 3 3 1 3 3.571429
## 103 2 2 2 4 5 2 4 4.142857
## 104 2 2 3 2 2 1 2 3.428571
## 105 2 2 2 2 2 1 3 3.571429
## 106 4 2 2 2 2 3 2 4.000000
## 107 3 3 3 3 1 2 2 4.000000
## 108 2 2 2 2 2 1 3 3.571429
## 109 3 1 1 1 1 1 1 3.000000
## 110 2 2 1 2 1 NA 2 3.428571
## 111 3 1 2 1 2 3 3 4.000000
## 112 3 2 3 1 3 2 1 4.000000
## 113 3 2 1 1 1 1 1 3.428571
## 114 3 2 1 2 2 1 2 3.857143
## 115 3 1 2 2 2 3 1 4.142857
## 116 2 3 1 4 3 1 1 4.285714
## 117 3 2 2 1 1 2 3 4.142857
## 118 2 2 2 2 2 2 2 4.142857
## 119 3 2 2 2 3 2 1 4.285714
## 120 3 2 2 2 3 1 2 4.285714
## 121 2 2 2 2 3 2 3 4.428571
## 122 3 2 2 1 1 1 2 4.000000
## 123 1 3 4 2 2 2 2 4.571429
## 124 3 2 2 1 1 2 2 4.142857
## 125 3 2 2 2 2 2 1 4.428571
## 126 2 3 2 2 1 2 2 4.428571
## 127 2 2 1 2 1 2 2 4.142857
## 128 3 3 2 1 2 1 1 4.285714
## 129 2 3 2 1 2 2 2 4.428571
## 130 2 2 2 2 2 1 1 4.285714
## 131 3 2 2 1 1 1 1 4.142857
## 132 2 2 2 2 1 1 1 4.285714
## 133 2 2 2 2 1 1 1 4.285714
## 134 3 3 1 1 1 1 1 4.285714
## 135 3 2 1 5 1 1 1 4.714286
## 136 3 2 2 2 2 1 1 4.571429
## 137 4 2 1 1 1 1 1 4.428571
## 138 2 2 2 1 2 2 1 4.571429
## 139 3 2 1 1 1 2 1 4.428571
## 140 2 2 2 1 1 2 3 4.714286
## 141 2 2 1 2 2 2 1 4.571429
## 142 4 2 2 1 1 1 1 4.571429
## 143 4 1 2 1 1 1 1 4.571429
## 144 2 2 2 1 2 1 1 4.571429
## 145 3 2 1 1 1 1 1 4.571429
## 146 2 2 1 2 1 1 1 4.714286
## 147 1 1 1 1 1 1 1 4.428571
## 148 3 1 1 1 1 1 1 4.857143
## 149 2 1 2 1 1 1 1 4.857143
## 150 2 1 1 1 1 1 2 4.857143
## 151 1 1 1 1 1 1 1 4.571429
## 152 2 1 2 1 1 1 1 4.857143
## 153 2 1 1 1 1 1 1 4.857143
## 154 1 1 1 2 1 1 1 5.000000
## 155 1 1 1 1 1 1 1 5.000000
## Avg_DisLikelihood Diff_Likelihood abs_diff
## 1 5.000000 -4.0000000 4.0000000
## 2 4.571429 -3.5714286 3.5714286
## 3 4.714286 -3.5714286 3.5714286
## 4 4.571429 -3.4285714 3.4285714
## 5 4.571429 -3.1428571 3.1428571
## 6 4.285714 -3.1428571 3.1428571
## 7 3.857143 -2.8571429 2.8571429
## 8 4.571429 -2.7142857 2.7142857
## 9 4.428571 -2.7142857 2.7142857
## 10 4.428571 -2.4285714 2.4285714
## 11 4.428571 -2.2857143 2.2857143
## 12 4.142857 -2.2857143 2.2857143
## 13 4.142857 -2.1428571 2.1428571
## 14 4.142857 -2.1428571 2.1428571
## 15 4.428571 -2.0000000 2.0000000
## 16 4.142857 -2.0000000 2.0000000
## 17 4.571429 -2.0000000 2.0000000
## 18 3.428571 -2.0000000 2.0000000
## 19 4.571429 -2.0000000 2.0000000
## 20 4.000000 -2.0000000 2.0000000
## 21 4.000000 -1.8571429 1.8571429
## 22 4.571429 -1.8571429 1.8571429
## 23 4.285714 -1.8571429 1.8571429
## 24 3.857143 -1.8571429 1.8571429
## 25 3.714286 -1.8571429 1.8571429
## 26 4.285714 -1.8571429 1.8571429
## 27 3.571429 -1.7142857 1.7142857
## 28 3.857143 -1.7142857 1.7142857
## 29 3.714286 -1.7142857 1.7142857
## 30 3.714286 -1.7142857 1.7142857
## 31 4.428571 -1.7142857 1.7142857
## 32 3.714286 -1.7142857 1.7142857
## 33 3.857143 -1.5714286 1.5714286
## 34 3.285714 -1.5714286 1.5714286
## 35 4.142857 -1.5714286 1.5714286
## 36 3.714286 -1.5714286 1.5714286
## 37 3.857143 -1.4285714 1.4285714
## 38 4.000000 -1.4285714 1.4285714
## 39 3.857143 -1.4285714 1.4285714
## 40 3.857143 -1.4285714 1.4285714
## 41 3.571429 -1.2857143 1.2857143
## 42 4.000000 -1.2857143 1.2857143
## 43 3.857143 -1.2857143 1.2857143
## 44 4.142857 -1.2857143 1.2857143
## 45 3.857143 -1.2857143 1.2857143
## 46 3.857143 -1.2857143 1.2857143
## 47 4.142857 -1.1428571 1.1428571
## 48 3.857143 -1.1428571 1.1428571
## 49 3.714286 -1.1428571 1.1428571
## 50 2.857143 -1.1428571 1.1428571
## 51 4.000000 -1.1428571 1.1428571
## 52 4.000000 -1.0000000 1.0000000
## 53 3.714286 -1.0000000 1.0000000
## 54 4.000000 -1.0000000 1.0000000
## 55 3.428571 -1.0000000 1.0000000
## 56 3.000000 -1.0000000 1.0000000
## 57 4.000000 -1.0000000 1.0000000
## 58 3.857143 -1.0000000 1.0000000
## 59 3.714286 -1.0000000 1.0000000
## 60 3.857143 -0.8571429 0.8571429
## 61 3.428571 -0.8571429 0.8571429
## 62 4.000000 -0.8571429 0.8571429
## 63 3.428571 -0.8571429 0.8571429
## 64 3.428571 -0.8571429 0.8571429
## 65 3.428571 -0.8571429 0.8571429
## 66 3.714286 -0.8571429 0.8571429
## 67 4.000000 -0.7142857 0.7142857
## 68 3.857143 -0.7142857 0.7142857
## 69 3.714286 -0.7142857 0.7142857
## 70 4.000000 -0.7142857 0.7142857
## 71 3.571429 -0.7142857 0.7142857
## 72 3.714286 -0.5714286 0.5714286
## 73 3.142857 -0.5714286 0.5714286
## 74 2.714286 -0.4285714 0.4285714
## 75 3.428571 -0.4285714 0.4285714
## 76 3.571429 -0.2857143 0.2857143
## 77 3.000000 -0.2857143 0.2857143
## 78 2.714286 -0.2857143 0.2857143
## 79 2.571429 -0.1428571 0.1428571
## 80 3.428571 -0.1428571 0.1428571
## 81 3.285714 -0.1428571 0.1428571
## 82 3.142857 -0.1428571 0.1428571
## 83 3.714286 -0.1428571 0.1428571
## 84 3.571429 -0.1428571 0.1428571
## 85 3.428571 0.0000000 0.0000000
## 86 3.285714 0.0000000 0.0000000
## 87 3.000000 0.0000000 0.0000000
## 88 3.714286 0.1428571 0.1428571
## 89 3.285714 0.1428571 0.1428571
## 90 2.571429 0.1428571 0.1428571
## 91 2.857143 0.1428571 0.1428571
## 92 3.428571 0.2857143 0.2857143
## 93 3.000000 0.4285714 0.4285714
## 94 2.857143 0.4285714 0.4285714
## 95 2.714286 0.5714286 0.5714286
## 96 3.000000 0.7142857 0.7142857
## 97 2.857143 0.8571429 0.8571429
## 98 2.571429 1.0000000 1.0000000
## 99 2.571429 1.0000000 1.0000000
## 100 2.142857 1.0000000 1.0000000
## 101 2.571429 1.0000000 1.0000000
## 102 2.428571 1.1428571 1.1428571
## 103 3.000000 1.1428571 1.1428571
## 104 2.000000 1.4285714 1.4285714
## 105 2.000000 1.5714286 1.5714286
## 106 2.428571 1.5714286 1.5714286
## 107 2.428571 1.5714286 1.5714286
## 108 2.000000 1.5714286 1.5714286
## 109 1.285714 1.7142857 1.7142857
## 110 1.666667 1.7619048 1.7619048
## 111 2.142857 1.8571429 1.8571429
## 112 2.142857 1.8571429 1.8571429
## 113 1.428571 2.0000000 2.0000000
## 114 1.857143 2.0000000 2.0000000
## 115 2.000000 2.1428571 2.1428571
## 116 2.142857 2.1428571 2.1428571
## 117 2.000000 2.1428571 2.1428571
## 118 2.000000 2.1428571 2.1428571
## 119 2.142857 2.1428571 2.1428571
## 120 2.142857 2.1428571 2.1428571
## 121 2.285714 2.1428571 2.1428571
## 122 1.714286 2.2857143 2.2857143
## 123 2.285714 2.2857143 2.2857143
## 124 1.857143 2.2857143 2.2857143
## 125 2.000000 2.4285714 2.4285714
## 126 2.000000 2.4285714 2.4285714
## 127 1.714286 2.4285714 2.4285714
## 128 1.857143 2.4285714 2.4285714
## 129 2.000000 2.4285714 2.4285714
## 130 1.714286 2.5714286 2.5714286
## 131 1.571429 2.5714286 2.5714286
## 132 1.571429 2.7142857 2.7142857
## 133 1.571429 2.7142857 2.7142857
## 134 1.571429 2.7142857 2.7142857
## 135 2.000000 2.7142857 2.7142857
## 136 1.857143 2.7142857 2.7142857
## 137 1.571429 2.8571429 2.8571429
## 138 1.714286 2.8571429 2.8571429
## 139 1.571429 2.8571429 2.8571429
## 140 1.857143 2.8571429 2.8571429
## 141 1.714286 2.8571429 2.8571429
## 142 1.714286 2.8571429 2.8571429
## 143 1.571429 3.0000000 3.0000000
## 144 1.571429 3.0000000 3.0000000
## 145 1.428571 3.1428571 3.1428571
## 146 1.428571 3.2857143 3.2857143
## 147 1.000000 3.4285714 3.4285714
## 148 1.285714 3.5714286 3.5714286
## 149 1.285714 3.5714286 3.5714286
## 150 1.285714 3.5714286 3.5714286
## 151 1.000000 3.5714286 3.5714286
## 152 1.285714 3.5714286 3.5714286
## 153 1.142857 3.7142857 3.7142857
## 154 1.142857 3.8571429 3.8571429
## 155 1.000000 4.0000000 4.0000000exemplar_goals <- goals %>%
filter(CASE_LBL %in% c("Hon_CorrectInaccurate", "Hon_NeedToKnow", "Hon_BeYourself", "Hon_Chest", "Hon_RightThing", "Hon_AvoidMis", "Hon_TreatResp", "Hon_HelpProduc", "Hon_FeelClose", "Hon_AvoidGuilt", "Hon_PutInPlace", "Hon_WasteTime", "Hon_GainConfi", "Hon_GetOtherDo", "Hon_ToLookGood", "Hon_AvoidDisap", "Hon_Story", "Hon_GentleOther", "Hon_KeepBrief", "Hon_AvoidAwk", "Hon_CoverUp", "Hon_GetSometh"))
ggplot(data = exemplar_goals, aes(x = Diff_Likelihood, y = reorder(CASE_LBL, Diff_Likelihood)))+
geom_point()
exemplar_goals <- exemplar_goals %>%
mutate(
R1_Dif = R1_Hon - R1_Dis,
R2_Dif = R2_Hon - R2_Dis,
R3_Dif = R3_Hon - R3_Dis,
R4_Dif = R4_Hon - R4_Dis,
R5_Dif = R5_Hon - R5_Dis,
R6_Dif = R6_Hon - R6_Dis,
R7_Dif = R7_Hon - R7_Dis)
exemplar_goals <- exemplar_goals %>%
group_by(CASE_LBL) %>%
mutate(sd = sd(unlist(select(cur_data(), R1_Dif:R7_Dif))))
ggplot(data = exemplar_goals, aes(x = Diff_Likelihood, y = reorder(CASE_LBL, Diff_Likelihood)))+
geom_point()+
geom_errorbar(aes(xmin = Diff_Likelihood - sd, xmax = Diff_Likelihood + sd))